Bottom Line:
Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence.But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy.Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits.

fig04: Predicted vs. observed age, for C18 males (A), anion exchange (AE) males (B), C18 females (C), and AE females (D). In each case, two-thirds of all samples were chosen at random to construct a model using partial least squares regression. This model was then used to predict values for the remaining one-third of the samples. Predictions were repeated twenty times in each case, and the figures shown here represent cases close to the mean R2 value. The red line represents a second-order polynomial fit to the data, and the dashed gray line is the isometric line. These results are consistent with R2 values obtained from ten-fold cross-validation scores based on sparse PLS on the full dataset (R2 values: AE males: 0.92; AE females 0.83; C18 males: 0.88; C18 females: 0.78). In all four cases, R2 values for permuted datasets were < 0.05.

Mentions:
Supervised multivariate analysis revealed that the metabolome is strongly predictive of sex and age. Partial least squares discriminant analysis differentiated almost completely between the metabolome of males and females (Fig. 3) and among flies of different ages (Fig. S2, Supporting information). Similarly, using partial least squares regression, we found the metabolome to be an accurate predictor of age. For both males and females, a model based on a random sample of two-thirds of all individuals was able to explain between 78% and 92% of the variance in age of the remaining one-third of samples (Fig. 4).

fig04: Predicted vs. observed age, for C18 males (A), anion exchange (AE) males (B), C18 females (C), and AE females (D). In each case, two-thirds of all samples were chosen at random to construct a model using partial least squares regression. This model was then used to predict values for the remaining one-third of the samples. Predictions were repeated twenty times in each case, and the figures shown here represent cases close to the mean R2 value. The red line represents a second-order polynomial fit to the data, and the dashed gray line is the isometric line. These results are consistent with R2 values obtained from ten-fold cross-validation scores based on sparse PLS on the full dataset (R2 values: AE males: 0.92; AE females 0.83; C18 males: 0.88; C18 females: 0.78). In all four cases, R2 values for permuted datasets were < 0.05.

Mentions:
Supervised multivariate analysis revealed that the metabolome is strongly predictive of sex and age. Partial least squares discriminant analysis differentiated almost completely between the metabolome of males and females (Fig. 3) and among flies of different ages (Fig. S2, Supporting information). Similarly, using partial least squares regression, we found the metabolome to be an accurate predictor of age. For both males and females, a model based on a random sample of two-thirds of all individuals was able to explain between 78% and 92% of the variance in age of the remaining one-third of samples (Fig. 4).

Bottom Line:
Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence.But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy.Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits.